ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control

Chetan Borse; Zhixian Xie; Wei-Cheng Huang; Wanxin Jin

cs.ROMar 12, 2026

ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control

Authors: Chetan Borse, Zhixian Xie, Wei-Cheng Huang, Wanxin Jin

Organizations: Intelligent Robotics and Interactive Systems (IRIS) Lab, Arizona State University · Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign

Abstract

Physics simulation for contact-rich robotics is often bottlenecked by contact resolution: mainstream engines enforce non-penetration and Coulomb friction via complementarity constraints or constrained optimization, requiring per-step iterative solves whose cost grows superlinearly with contact density. We present ComFree-Sim, a GPU-parallelized analytical contact physics engine built on complementarity-free contact modeling. ComFree-Sim computes contact impulses in closed form via an impedance-style prediction--correction update in the dual cone of Coulomb friction. Contact computation decouples across contact pairs and becomes separable across cone facets, mapping naturally to GPU kernels and yielding near-linear runtime scaling with the number of contacts. We further extend the formulation to a unified 6D contact model capturing tangential, torsional, and rolling friction, and introduce a practical dual-cone impedance heuristic. ComFree-Sim is implemented in Warp and exposed through a MuJoCo-compatible interface as a drop-in backend alternative to MuJoCo Warp (MJWarp). Experiments benchmark penetration, friction behaviors, stability, and simulation runtime scaling against MJWarp, demonstrating near-linear scaling and 2--3 times higher throughput in dense contact scenes with comparable physical fidelity. We deploy ComFree-Sim in real-time MPC for in-hand dexterous manipulation on a real-world multi-fingered LEAP hand and in dynamics-aware motion retargeting, demonstrating that low-latency simulation yields higher closed-loop success rates and enables practical high-frequency control in contact-rich tasks.

Figures & tables

Fig. 1 : Performance overview of the ComFree-Sim. In the second row, it shows 2–3 $\times$ higher throughput than MuJoCo Warp (MJWarp) in dense contact simulation; in the first row, it enables low-latency model predictive control for in-hand dexterous manipulation; and in the third row, it is used for dynamics-aware motion retargeting with MuJoCo as rollout environments.

Fig. 2 : Different contact modes captured by ComFree-Sim.

Panel of Figure 3 — Fig. 3 : Penetration-depth benchmark in a collision-rich drop test: (a) stacked arrays of primitives released to free-fall onto a flat box; (b) mean $\pm$ std penetration depth over all detected contacts. In all tests, simulation timestep $dt=0.002$ .

Panel of Figure 4 — Fig. 4 : Isolated benchmark environments.

Panel of Figure 5 — Fig. 5 : Results of torsional and rolling friction test.

Fig. 6 : Numerical stability test: horizontal speed and vertical position $z$ of free-fall-and-sliding cube over time. Left: ComFree-Sim with varying $(k_{\text{user}},d_{\text{user}})$ at $dt{=}0.002$ s. Right: ComFree-Sim under varying timestep $dt$ .

Fig. 7 : Runtime scaling benchmark. Y: wall-clock step time.

Panel of Figure 8 — Fig. 8 : Allegro hand throughput test.

	Pos Error [m]		Quat Error		MPPI Time [ms]		Task completion time [s]		Success Rate (%)
Object	ComFree	MJWarp	ComFree	MJWarp	ComFree	MJWarp	ComFree	MJWarp	ComFree	MJWarp
Cube (Yaw)	0.0106 $\pm$ 0.0028	0.0119 $\pm$ 0.0041	0.0231 $\pm$ 0.0081	0.0236 $\pm$ 0.0103	28.2308 $\pm$ 2.0053	54.5961 $\pm$ 5.5158	42.4261 $\pm$ 21.3724	60.6831 $\pm$ 40.9661	80.00	65.00
Cube (Roll/Pitch)	0.0087 $\pm$ 0.0036	0.0114 $\pm$ 0.0032	0.0250 $\pm$ 0.0084	0.0291 $\pm$ 0.0054	27.7969 $\pm$ 1.0890	56.4415 $\pm$ 5.5757	20.1756 $\pm$ 12.0413	74.3734 $\pm$ 44.2553	55.00	30.00
Duck (Yaw)	0.0150 $\pm$ 0.0020	0.0116 $\pm$ 0.0028	0.0255 $\pm$ 0.0066	0.0186 $\pm$ 0.0104	18.2271 $\pm$ 0.7618	41.8674 $\pm$ 9.3805	28.7974 $\pm$ 18.4190	49.4049 $\pm$ 29.3045	50.00	25.00
Duck (Roll/Pitch)	0.0183 $\pm$ 0.0012	0.0180 $\pm$ 0.0000	0.0191 $\pm$ 0.0036	0.0195 $\pm$ 0.0000	13.9000 $\pm$ 5.8266	49.2301 $\pm$ 0.0000	24.8061 $\pm$ 15.4174	7.6724 $\pm$ 0.0000	20.00	10.00
SpamTin (Yaw)	0.0161 $\pm$ 0.0013	0.0164 $\pm$ 0.0011	0.0194 $\pm$ 0.0085	0.0172 $\pm$ 0.0036	20.0710 $\pm$ 0.8337	52.4135 $\pm$ 2.4810	29.3871 $\pm$ 18.3080	53.0317 $\pm$ 21.1797	65.00	15.00

TABLE I : Control and runtime performance for real-world in-hand dexterous manipulation with LEAP Hand

Fig. 9 : Hardware setup and system diagram.

Pos. Error

Quat. Error

Joint Error

Obj. Error

Obj. Quat. Error

MPPI. Time (s)

Task

ComFree

MJWarp

ComFree

MJWarp

ComFree

MJWarp

ComFree

MJWarp

ComFree

MJWarp

ComFree

MJWarp

Cartwheels

0.037

\pm

0.002

0.033

\pm

0.001

0.100

\pm

0.004

0.074

\pm

0.001

0.692

\pm

0.008

0.682

\pm

0.007

–

0.529

\pm

0.422

0.705

\pm

0.490

Martial Arts

0.047

\pm

0.003

0.028

\pm

0.001

0.118

\pm

0.005

0.076

\pm

0.002

0.830

\pm

0.007

0.794

\pm

0.007

–

0.589

\pm

0.336

0.774

\pm

0.492

Dance

0.037

\pm

0.001

0.033

\pm

0.000

0.094

\pm

0.003

0.069

\pm

0.000

0.881

\pm

0.003

0.871

\pm

0.001

–

0.481

\pm

0.367

0.646

\pm

0.428

Getup

0.036

\pm

0.001

0.032

\pm

0.000

0.088

\pm

0.003

0.068

\pm

0.001

0.705

\pm

0.003

0.682

\pm

0.003

–

0.473

\pm

0.388

0.572

\pm

0.342

PushBox

0.103

\pm

0.017

0.080

\pm

0.005

0.079

\pm

0.010

0.057

\pm

0.002

0.246

\pm

0.022

0.196

\pm

0.004

0.169

\pm

0.082

0.098

\pm

0.010

0.260

\pm

0.146

0.161

\pm

0.018

1.171

\pm

1.189

0.719

\pm

0.708

TABLE II : Comparison of Motion Retargeting Performance Across Tasks

Panel of Figure 12 — Fig. 10 : Retargeting Tasks.

Robotics Simulation

Robotics simulation builds virtual robots and environments to model, train, and evaluate autonomous systems before real-world deployment.

See all 306 papers

Oct 15, 2026

CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation

Efficient synthesis of contact-rich manipulation trajectories is essential for downstream robot learning. Gradient-based planning offers a promising approach, but jointly optimizing motion and contact decisions remains challenging. Meanwhile, many existing planners run on CPUs with limited throughput. We present CQDC-Warp, a GPU-accelerated differentiable quasi-dynamic simulator for batched model predictive control (MPC). We use it to study contact-guided trajectory optimization, in which intended robot--object contacts are specified before planning and encouraged through soft costs, while the simulated dynamics determine contact geometry and frictional response without a prescribed contact-mode schedule. Across

17{,}408

trials on the

17

-object Allegro in-hand rotation benchmark, our framework achieves

79.8\%

success, compared with

73.7\%

for CFree and

65.2\%

for IMPACT, and reduces mean trajectory-generation time by factors of

27.0

and

50.2

, respectively. On a single NVIDIA RTX 4090 with

2{,}048

parallel worlds, forward and backward throughput reach

2.06\times10^5

and

2.25\times10^5

world-steps per second, respectively. Further experiments cover manipulation with LEAP and Shadow hands, environmental contact exploitation, bimanual manipulation, whole-body control, and real-world trajectory replay on a Sharpa hand.

Yanchen Ding, Jiayi Chen, Jianfeng Xu +3

Peking University · Galbot · Tsinghua University

Contact-Rich Robotic Manipulation Trajectory Optimization Differentiable Physics Model Predictive Control

Ask PDF

Sep 18, 2026

CRISP: Contact-Rich Robotic Simulation Platform with Extensive Geometries and Contact Solvers

We present CRISP (Contact-RIch Simulation Platform), a high-fidelity physics engine tailored for complex multi-contact simulations such as tight-tolerance robotic manipulation. Achieving high physical fidelity in robotic simulation requires both expressive modeling of geometry and contact interactions, as well as accurate numerical resolution via robust collision detection and contact solvers. However, existing simulators often either rely on limited support for geometric representations and simplified modeling of contact interactions, or employ numerical resolution methods whose accuracy or robustness is inherently constrained. Accordingly, we develop a new simulator that supports diverse geometric representations with accurate optimization-based collision detection, and combines contact modeling with robust augmented Lagrangian-based contact solvers. This integration enables efficient and consistent detection of contact information across complex geometries while accurately resolving multi-contact constraints without problematic relaxations, which is essential for simulating contact-intensive and sharp interactions. We validate the physical fidelity of our simulator against state-of-the-art platforms and further demonstrate its capabilities through complex robotic manipulation scenarios. CRISP is publicly available at https://github.com/INRoL/crisp.

Somang Lee, Sunkyung Park, Jinhee Yun +2

Department of Mechanical Engineering, Seoul National University

Robotics Simulation Physics-Based Simulation Contact-Rich Robotic Manipulation

Ask PDF

Sep 11, 2024

Highly-Efficient Differentiable Simulation for Robotics

Robotics simulators have improved significantly in computational speed and scalability, enabling them to generate years of simulated data for complex systems in minutes or hours. Despite these advances, efficiently and accurately computing simulation derivatives remains an open challenge. Addressing this would accelerate the convergence of reinforcement learning and trajectory optimization algorithms, particularly for contact-rich problems. This paper introduces a unifying framework for robotic simulation that accounts for all factors, including dynamics, collisions, and friction. The resulting algorithm computes analytical derivatives of the simulation by implicit differentiation, explicitly handling the intrinsic non-smoothness of the collision and frictional stages while exploiting the sparsity induced by the multi-body structure. Benchmark results demonstrate state-of-the-art performance, with timings ranging from

5\,μ

s for a 7-dof manipulator to

95\,μ

s for a 36-dof humanoid, an improvement of at least two orders of magnitude over alternative methods. Implemented in C++, the code will be open-sourced after the review process to support applications such as simulation-driven learning and real-time control.

Quentin Le Lidec, Louis Montaut, Yann de Mont-Marin +3

Inria, Ecole normale sup´erieure CNRS, PSL Research University 75005 Paris, France

Robotics Simulation Multibody Dynamics Differentiable Physics

Ask PDF

Abstract

Robotics Simulation

Explore similar work

CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation

CRISP: Contact-Rich Robotic Simulation Platform with Extensive Geometries and Contact Solvers

Highly-Efficient Differentiable Simulation for Robotics